{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Matthew\Dropbox\DHN Philippine Police\3. EGAP (POP - One Sorsogon Reloaded)\3.12 - Articles and Chapters\3.12.5 Self Enumeration and Sensitive Items\Replication Materials\SelfEnumerationdo_rep_log.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 1 May 2020, 09:35:32

{com}. do "C:\Users\Matthew\Dropbox\DHN Philippine Police\3. EGAP (POP - One Sorsogon Reloaded)\3.12 - Articles and Chapters\3.12.5 Self Enumeration and Sensitive Items\Replication Materials\SelfEnumerationdo_rep.do"
{txt}
{com}. ***REPLICATION CODE for Nanes and Haim, "Self Administered Field Surveys on Sensitive Topics"
. ***Journal of Experimental Political Science - Conditionally accepted April 28, 2020
. ***Corresponding author: Matthew Nanes
. 
. *Open data
. cd "~\Dropbox\DHN Philippine Police\3. EGAP (POP - One Sorsogon Reloaded)\3.12 - Articles and Chapters\3.12.5 Self Enumeration and Sensitive Items\Replication Materials"
{res}C:\Users\Matthew\Dropbox\DHN Philippine Police\3. EGAP (POP - One Sorsogon Reloaded)\3.12 - Articles and Chapters\3.12.5 Self Enumeration and Sensitive Items\Replication Materials
{txt}
{com}. use "NanesandHaim JEPS Replication.dta"
{txt}
{com}. 
. *Generate and lable interaction terms to be used later
. gen selfenum_education=selfenum*education
{txt}(9 missing values generated)

{com}. label variable selfenum_education "Self x Education"
{txt}
{com}. gen rr_education=randomresp*education
{txt}(9 missing values generated)

{com}. label variable rr_education "RR x Education"
{txt}
{com}. 
. **********************ANALYSIS*********************
. 
. **Non-Response**
. 
. *FIGURE 2 (main text): Response Rates by Experimental Group
. *(0+1="Answered, 97="Don't Know", 98="Refuse to Answer")
. tab placdir

   {txt}plac.dir {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        749       47.44       47.44
{txt}          1 {c |}{res}        823       52.12       99.56
{txt}         97 {c |}{res}          4        0.25       99.81
{txt}         98 {c |}{res}          3        0.19      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,579      100.00
{txt}
{com}. tab placself

  {txt}plac.self {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        710       48.14       48.14
{txt}          1 {c |}{res}        763       51.73       99.86
{txt}         98 {c |}{res}          2        0.14      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,475      100.00
{txt}
{com}. tab placrr

    {txt}plac.rr {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        533       36.78       36.78
{txt}          1 {c |}{res}        899       62.04       98.83
{txt}         97 {c |}{res}          8        0.55       99.38
{txt}         98 {c |}{res}          9        0.62      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,449      100.00
{txt}
{com}. 
. *(0+1="Answered, 97="Don't Know", 98="Refuse to Answer")
. tab sensitivedir

{txt}sensitive.d {c |}
         ir {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        435       27.55       27.55
{txt}          1 {c |}{res}        686       43.45       70.99
{txt}         97 {c |}{res}        289       18.30       89.30
{txt}         98 {c |}{res}        169       10.70      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,579      100.00
{txt}
{com}. tab sensitiveself

{txt}sensitive.s {c |}
        elf {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        429       29.08       29.08
{txt}          1 {c |}{res}        667       45.22       74.31
{txt}         97 {c |}{res}        233       15.80       90.10
{txt}         98 {c |}{res}        146        9.90      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,475      100.00
{txt}
{com}. tab sensitiverr

{txt}sensitive.r {c |}
          r {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        364       25.12       25.12
{txt}          1 {c |}{res}        782       53.97       79.09
{txt}         97 {c |}{res}        176       12.15       91.24
{txt}         98 {c |}{res}        127        8.76      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,449      100.00
{txt}
{com}. 
. *TABLE 1(appendix): Non-Response to Sensitive Question
. xtset psgc
{txt}{col 8}panel variable:  {res}psgc (unbalanced)
{txt}
{com}. eststo clear
{txt}
{com}. eststo: logit sensitivenr i.sensitivecat, cl(psgc)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2546.3564}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2533.0854}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2533.0451}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2533.0451}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,502
{txt}{col 49}Wald chi2({res}2{txt}){col 67}= {res}     28.45
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-2533.0451{txt}{col 49}Pseudo R2{col 67}= {res}    0.0052

{txt}{ralign 78:(Std. Err. adjusted for {res:302} clusters in psgc)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1} sensitivenr{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
sensitivecat {c |}
{space 7}Self  {c |}{col 14}{res}{space 2} -.169421{col 26}{space 2} .0860174{col 37}{space 1}   -1.97{col 46}{space 3}0.049{col 54}{space 4}-.3380121{col 67}{space 3}  -.00083
{txt}Rand. Resp.  {c |}{col 14}{res}{space 2}-.4351929{col 26}{space 2} .0828588{col 37}{space 1}   -5.25{col 46}{space 3}0.000{col 54}{space 4}-.5975932{col 67}{space 3}-.2727925
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-.8951072{col 26}{space 2} .0663149{col 37}{space 1}  -13.50{col 46}{space 3}0.000{col 54}{space 4}-1.025082{col 67}{space 3}-.7651323
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}. eststo: xtlogit sensitivenr i.sensitivecat, fe
{txt}note: multiple positive outcomes within groups encountered.
note: 21 groups (285 obs) dropped because of all positive or
      all negative outcomes.
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: -1813.956}  
Iteration 1:{space 3}log likelihood = {res:-1813.9508}  
Iteration 2:{space 3}log likelihood = {res:-1813.9508}  
{res}
{txt}Conditional fixed-effects logistic regression{col 49}Number of obs{col 67}={col 69}{res}     4,217
{txt}Group variable: {res}psgc{col 49}{txt}Number of groups{col 67}={col 69}{res}       281

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        12
{txt}{col 63}avg{col 67}={col 69}{res}      15.0
{txt}{col 63}max{col 67}={col 69}{res}        18

{txt}{col 49}LR chi2({res}2{txt}){col 67}={col 70}{res}    26.45
{txt}Log likelihood  = {res}-1813.9508{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} sensitivenr{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
sensitivecat {c |}
{space 7}Self  {c |}{col 14}{res}{space 2}-.1790667{col 26}{space 2} .0859612{col 37}{space 1}   -2.08{col 46}{space 3}0.037{col 54}{space 4}-.3475475{col 67}{space 3}-.0105859
{txt}Rand. Resp.  {c |}{col 14}{res}{space 2}-.4538817{col 26}{space 2} .0890799{col 37}{space 1}   -5.10{col 46}{space 3}0.000{col 54}{space 4}-.6284751{col 67}{space 3}-.2792882
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. eststo: xtlogit sensitivenr i.sensitivecat crowd selfenum_crowd education male age income, fe
{txt}note: multiple positive outcomes within groups encountered.
note: 21 groups (284 obs) dropped because of all positive or
      all negative outcomes.
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-1771.5469}  
Iteration 1:{space 3}log likelihood = {res:-1770.8472}  
Iteration 2:{space 3}log likelihood = {res:-1770.8472}  
{res}
{txt}Conditional fixed-effects logistic regression{col 49}Number of obs{col 67}={col 69}{res}     4,141
{txt}Group variable: {res}psgc{col 49}{txt}Number of groups{col 67}={col 69}{res}       281

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        11
{txt}{col 63}avg{col 67}={col 69}{res}      14.7
{txt}{col 63}max{col 67}={col 69}{res}        18

{txt}{col 49}LR chi2({res}8{txt}){col 67}={col 70}{res}    50.13
{txt}Log likelihood  = {res}-1770.8472{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   sensitivenr{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}sensitivecat {c |}
{space 9}Self  {c |}{col 16}{res}{space 2}-.1808488{col 28}{space 2} .0990913{col 39}{space 1}   -1.83{col 48}{space 3}0.068{col 56}{space 4}-.3750642{col 69}{space 3} .0133667
{txt}{space 2}Rand. Resp.  {c |}{col 16}{res}{space 2}-.4741504{col 28}{space 2} .0899764{col 39}{space 1}   -5.27{col 48}{space 3}0.000{col 56}{space 4}-.6505009{col 69}{space 3}-.2977999
{txt}{space 14} {c |}
{space 9}crowd {c |}{col 16}{res}{space 2} .3021976{col 28}{space 2}  .108801{col 39}{space 1}    2.78{col 48}{space 3}0.005{col 56}{space 4} .0889516{col 69}{space 3} .5154437
{txt}selfenum_crowd {c |}{col 16}{res}{space 2}-.0605122{col 28}{space 2} .1780855{col 39}{space 1}   -0.34{col 48}{space 3}0.734{col 56}{space 4}-.4095534{col 69}{space 3}  .288529
{txt}{space 5}education {c |}{col 16}{res}{space 2} .1140976{col 28}{space 2}  .031941{col 39}{space 1}    3.57{col 48}{space 3}0.000{col 56}{space 4} .0514945{col 69}{space 3} .1767008
{txt}{space 10}male {c |}{col 16}{res}{space 2}-.0466308{col 28}{space 2} .0804649{col 39}{space 1}   -0.58{col 48}{space 3}0.562{col 56}{space 4}-.2043391{col 69}{space 3} .1110775
{txt}{space 11}age {c |}{col 16}{res}{space 2}  .001902{col 28}{space 2} .0026247{col 39}{space 1}    0.72{col 48}{space 3}0.469{col 56}{space 4}-.0032423{col 69}{space 3} .0070463
{txt}{space 8}income {c |}{col 16}{res}{space 2}-1.16e-06{col 28}{space 2} 4.19e-06{col 39}{space 1}   -0.28{col 48}{space 3}0.783{col 56}{space 4}-9.37e-06{col 69}{space 3} 7.06e-06
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}. *esttab using "nonresp_sensitive.tex", se star(* .10 ** .05 *** .01) nodep mtitle("" "" "" "") label title(Non-Response to Sensitive Question\label{c -(}tab:nonresp{c )-}) note(Logistic regression with barangay-clustered SE.) replace
. 
. *TABLE 2(appendix): Would report anti-government group to the police
. xtset psgc
{txt}{col 8}panel variable:  {res}psgc (unbalanced)
{txt}
{com}. eststo clear
{txt}
{com}. eststo: logit reportNPA selfenum, cl(psgc)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1482.3323}  
Iteration 1:{space 3}log pseudolikelihood = {res: -1482.319}  
Iteration 2:{space 3}log pseudolikelihood = {res: -1482.319}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     2,217
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      0.02
{txt}{col 49}Prob > chi2{col 67}= {res}    0.8810
{txt}Log pseudolikelihood = {res} -1482.319{txt}{col 49}Pseudo R2{col 67}= {res}    0.0000

{txt}{ralign 78:(Std. Err. adjusted for {res:300} clusters in psgc)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   reportNPA{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}selfenum {c |}{col 14}{res}{space 2}-.0141985{col 26}{space 2} .0948817{col 37}{space 1}   -0.15{col 46}{space 3}0.881{col 54}{space 4}-.2001632{col 67}{space 3} .1717663
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .4555316{col 26}{space 2} .0679562{col 37}{space 1}    6.70{col 46}{space 3}0.000{col 54}{space 4}   .32234{col 67}{space 3} .5887232
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}. eststo: xtlogit reportNPA selfenum, fe
{txt}note: multiple positive outcomes within groups encountered.
note: 22 groups (142 obs) dropped because of all positive or
      all negative outcomes.
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-941.90616}  
Iteration 1:{space 3}log likelihood = {res:-941.84505}  
Iteration 2:{space 3}log likelihood = {res:-941.84505}  
{res}
{txt}Conditional fixed-effects logistic regression{col 49}Number of obs{col 67}={col 69}{res}     2,075
{txt}Group variable: {res}psgc{col 49}{txt}Number of groups{col 67}={col 69}{res}       278

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         2
{txt}{col 63}avg{col 67}={col 69}{res}       7.5
{txt}{col 63}max{col 67}={col 69}{res}        13

{txt}{col 49}LR chi2({res}1{txt}){col 67}={col 70}{res}     0.40
{txt}Log likelihood  = {res}-941.84505{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.5290

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   reportNPA{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}selfenum {c |}{col 14}{res}{space 2}-.0587992{col 26}{space 2} .0934038{col 37}{space 1}   -0.63{col 46}{space 3}0.529{col 54}{space 4}-.2418672{col 67}{space 3} .1242689
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. eststo: xtlogit reportNPA selfenum crowd selfenum_crowd male age education income, fe
{txt}note: multiple positive outcomes within groups encountered.
note: 24 groups (151 obs) dropped because of all positive or
      all negative outcomes.
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: -883.0244}  
Iteration 1:{space 3}log likelihood = {res:-881.48661}  
Iteration 2:{space 3}log likelihood = {res:-881.48493}  
Iteration 3:{space 3}log likelihood = {res:-881.48493}  
{res}
{txt}Conditional fixed-effects logistic regression{col 49}Number of obs{col 67}={col 69}{res}     2,016
{txt}Group variable: {res}psgc{col 49}{txt}Number of groups{col 67}={col 69}{res}       276

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         2
{txt}{col 63}avg{col 67}={col 69}{res}       7.3
{txt}{col 63}max{col 67}={col 69}{res}        13

{txt}{col 49}LR chi2({res}7{txt}){col 67}={col 70}{res}    63.14
{txt}Log likelihood  = {res}-881.48493{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     reportNPA{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}selfenum {c |}{col 16}{res}{space 2}-.1145323{col 28}{space 2} .1114371{col 39}{space 1}   -1.03{col 48}{space 3}0.304{col 56}{space 4}-.3329449{col 69}{space 3} .1038803
{txt}{space 9}crowd {c |}{col 16}{res}{space 2}-.2837206{col 28}{space 2} .1714232{col 39}{space 1}   -1.66{col 48}{space 3}0.098{col 56}{space 4}-.6197038{col 69}{space 3} .0522626
{txt}selfenum_crowd {c |}{col 16}{res}{space 2} .1756003{col 28}{space 2}  .232647{col 39}{space 1}    0.75{col 48}{space 3}0.450{col 56}{space 4}-.2803794{col 69}{space 3} .6315799
{txt}{space 10}male {c |}{col 16}{res}{space 2} .1745534{col 28}{space 2} .1069839{col 39}{space 1}    1.63{col 48}{space 3}0.103{col 56}{space 4}-.0351313{col 69}{space 3}  .384238
{txt}{space 11}age {c |}{col 16}{res}{space 2}   -.0169{col 28}{space 2} .0035404{col 39}{space 1}   -4.77{col 48}{space 3}0.000{col 56}{space 4}-.0238391{col 69}{space 3}-.0099608
{txt}{space 5}education {c |}{col 16}{res}{space 2} .1377368{col 28}{space 2} .0443069{col 39}{space 1}    3.11{col 48}{space 3}0.002{col 56}{space 4} .0508968{col 69}{space 3} .2245768
{txt}{space 8}income {c |}{col 16}{res}{space 2} 1.99e-06{col 28}{space 2} 6.34e-06{col 39}{space 1}    0.31{col 48}{space 3}0.753{col 56}{space 4}-.0000104{col 69}{space 3} .0000144
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}. *esttab using "nonresp_placebo.tex", se star(* .10 ** .05 *** .01) nodep order() mtitle("" "" "" "" "") label title(Non-Response to Placebo Question\label{c -(}tab:nonrespplac{c )-}) note(Logistic regression with barangay-clustered SE.) replace
. 
. **Falsification**
. 
. *FIGURE 3 (main text): Affirmative Answers by Experimental Group
. tab claimHS sensitivecat, column
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

           {c |}     sensitive.cat
  Claim HS {c |}    Direct       Self {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       749        710 {txt}{c |}{res}     1,459 
           {txt}{c |}{res}     47.65      48.20 {txt}{c |}{res}     47.91 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
         1 {c |}{res}       823        763 {txt}{c |}{res}     1,586 
           {txt}{c |}{res}     52.35      51.80 {txt}{c |}{res}     52.09 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}     1,572      1,473 {txt}{c |}{res}     3,045 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 
{txt}
{com}. tab reportNPA sensitivecat, column
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

           {c |}     sensitive.cat
Report NPA {c |}    Direct       Self {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       435        429 {txt}{c |}{res}       864 
           {txt}{c |}{res}     38.80      39.14 {txt}{c |}{res}     38.97 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
         1 {c |}{res}       686        667 {txt}{c |}{res}     1,353 
           {txt}{c |}{res}     61.20      60.86 {txt}{c |}{res}     61.03 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}     1,121      1,096 {txt}{c |}{res}     2,217 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 
{txt}
{com}. *To calculate "Forced" column, see Appendix C "Forced Choice Calculations"
. 
. *TABLE 3(appendix): Non-Response to Placebo QUestion
. xtset psgc
{txt}{col 8}panel variable:  {res}psgc (unbalanced)
{txt}
{com}. eststo clear
{txt}
{com}. eststo: logit placnr i.sensitivecat, cl(psgc)

{txt}note: 2.sensitivecat != 0 predicts failure perfectly
      2.sensitivecat dropped and 1474 obs not used

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-140.00713}  
Iteration 1:{space 3}log pseudolikelihood = {res:-137.52578}  
Iteration 2:{space 3}log pseudolikelihood = {res:-137.38728}  
Iteration 3:{space 3}log pseudolikelihood = {res:-137.38687}  
Iteration 4:{space 3}log pseudolikelihood = {res:-137.38687}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     3,028
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      5.10
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0239
{txt}Log pseudolikelihood = {res}-137.38687{txt}{col 49}Pseudo R2{col 67}= {res}    0.0187

{txt}{ralign 78:(Std. Err. adjusted for {res:302} clusters in psgc)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      placnr{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
sensitivecat {c |}
{space 7}Self  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (empty)
Rand. Resp.  {c |}{col 14}{res}{space 2} .9805798{col 26}{space 2} .4340549{col 37}{space 1}    2.26{col 46}{space 3}0.024{col 54}{space 4} .1298479{col 67}{space 3} 1.831312
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}-5.414194{col 26}{space 2}  .375981{col 37}{space 1}  -14.40{col 46}{space 3}0.000{col 54}{space 4}-6.151103{col 67}{space 3}-4.677285
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}. eststo: xtlogit placnr i.sensitivecat, fe
{txt}note: multiple positive outcomes within groups encountered.
note: 279 groups (4,158 obs) dropped because of all positive or
      all negative outcomes.
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:  -57.3916}  
Iteration 1:{space 3}log likelihood = {res:-51.735021}  
Iteration 2:{space 3}log likelihood = {res:-51.506002}  
Iteration 3:{space 3}log likelihood = {res:-51.462557}  
Iteration 4:{space 3}log likelihood = {res:-51.457144}  
Iteration 5:{space 3}log likelihood = {res:-51.456381}  
Iteration 6:{space 3}log likelihood = {res:-51.456247}  
Iteration 7:{space 3}log likelihood = {res:-51.456216}  
Iteration 8:{space 3}log likelihood = {res:-51.456209}  
Iteration 9:{space 3}log likelihood = {res:-51.456207}  
{res}
{txt}Conditional fixed-effects logistic regression{col 49}Number of obs{col 67}={col 69}{res}       344
{txt}Group variable: {res}psgc{col 49}{txt}Number of groups{col 67}={col 69}{res}        23

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        14
{txt}{col 63}avg{col 67}={col 69}{res}      15.0
{txt}{col 63}max{col 67}={col 69}{res}        16

{txt}{col 49}LR chi2({res}2{txt}){col 67}={col 70}{res}    25.40
{txt}Log likelihood  = {res}-51.456207{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      placnr{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
sensitivecat {c |}
{space 7}Self  {c |}{col 14}{res}{space 2}-17.13433{col 26}{space 2} 1950.616{col 37}{space 1}   -0.01{col 46}{space 3}0.993{col 54}{space 4}-3840.272{col 67}{space 3} 3806.003
{txt}Rand. Resp.  {c |}{col 14}{res}{space 2} .9861569{col 26}{space 2} .4545664{col 37}{space 1}    2.17{col 46}{space 3}0.030{col 54}{space 4} .0952231{col 67}{space 3} 1.877091
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. eststo: xtlogit placnr i.sensitivecat crowd selfenum_crowd male age education income, fe
{txt}note: multiple positive outcomes within groups encountered.
note: 279 groups (4,088 obs) dropped because of all positive or
      all negative outcomes.
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-52.680285}  
Iteration 1:{space 3}log likelihood = {res:-46.590277}  
Iteration 2:{space 3}log likelihood = {res:-46.170071}  
Iteration 3:{space 3}log likelihood = {res:-46.126001}  
Iteration 4:{space 3}log likelihood = {res:-46.118869}  
Iteration 5:{space 3}log likelihood = {res:-46.117136}  
Iteration 6:{space 3}log likelihood = {res:-46.116765}  
Iteration 7:{space 3}log likelihood = {res:-46.116687}  
Iteration 8:{space 3}log likelihood = {res:-46.116669}  
Iteration 9:{space 3}log likelihood = {res:-46.116665}  
{res}
{txt}Conditional fixed-effects logistic regression{col 49}Number of obs{col 67}={col 69}{res}       337
{txt}Group variable: {res}psgc{col 49}{txt}Number of groups{col 67}={col 69}{res}        23

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        14
{txt}{col 63}avg{col 67}={col 69}{res}      14.7
{txt}{col 63}max{col 67}={col 69}{res}        16

{txt}{col 49}LR chi2({res}8{txt}){col 67}={col 70}{res}    34.97
{txt}Log likelihood  = {res}-46.116665{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        placnr{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}sensitivecat {c |}
{space 9}Self  {c |}{col 16}{res}{space 2}-16.13602{col 28}{space 2} 1417.663{col 39}{space 1}   -0.01{col 48}{space 3}0.991{col 56}{space 4}-2794.704{col 69}{space 3} 2762.432
{txt}{space 2}Rand. Resp.  {c |}{col 16}{res}{space 2} 1.082711{col 28}{space 2} .4788551{col 39}{space 1}    2.26{col 48}{space 3}0.024{col 56}{space 4}  .144172{col 69}{space 3}  2.02125
{txt}{space 14} {c |}
{space 9}crowd {c |}{col 16}{res}{space 2} .3811158{col 28}{space 2} .5155907{col 39}{space 1}    0.74{col 48}{space 3}0.460{col 56}{space 4}-.6294235{col 69}{space 3} 1.391655
{txt}selfenum_crowd {c |}{col 16}{res}{space 2}-.0786193{col 28}{space 2} 2433.766{col 39}{space 1}   -0.00{col 48}{space 3}1.000{col 56}{space 4}-4770.172{col 69}{space 3} 4770.015
{txt}{space 10}male {c |}{col 16}{res}{space 2}-.1455609{col 28}{space 2} .4969196{col 39}{space 1}   -0.29{col 48}{space 3}0.770{col 56}{space 4}-1.119505{col 69}{space 3} .8283837
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0423706{col 28}{space 2} .0172186{col 39}{space 1}   -2.46{col 48}{space 3}0.014{col 56}{space 4}-.0761185{col 69}{space 3}-.0086227
{txt}{space 5}education {c |}{col 16}{res}{space 2}-.4249009{col 28}{space 2} .2115558{col 39}{space 1}   -2.01{col 48}{space 3}0.045{col 56}{space 4}-.8395426{col 69}{space 3}-.0102592
{txt}{space 8}income {c |}{col 16}{res}{space 2} .0000215{col 28}{space 2} .0000217{col 39}{space 1}    0.99{col 48}{space 3}0.322{col 56}{space 4} -.000021{col 69}{space 3}  .000064
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}. *esttab using "selfvdirect_sensitive.tex", se star(* .10 ** .05 *** .01) nodep order(selfenum crowd) mtitle("" "" "" "" "") label title(Would report anti-government group to the police\label{c -(}tab:selfvdirect{c )-}) note(Logistic regression with barangay-clustered SE.) replace
. 
. *TABLE 4(appendix): Did you graduate high school?
. xtset psgc
{txt}{col 8}panel variable:  {res}psgc (unbalanced)
{txt}
{com}. eststo clear
{txt}
{com}. eststo: logit claimHS selfenum, cl(psgc)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -2107.248}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2107.2069}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2107.2069}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     3,044
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      0.09
{txt}{col 49}Prob > chi2{col 67}= {res}    0.7636
{txt}Log pseudolikelihood = {res}-2107.2069{txt}{col 49}Pseudo R2{col 67}= {res}    0.0000

{txt}{ralign 78:(Std. Err. adjusted for {res:300} clusters in psgc)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     claimHS{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}selfenum {c |}{col 14}{res}{space 2}-.0208147{col 26}{space 2}  .069194{col 37}{space 1}   -0.30{col 46}{space 3}0.764{col 54}{space 4}-.1564325{col 67}{space 3}  .114803
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0942172{col 26}{space 2} .0566706{col 37}{space 1}    1.66{col 46}{space 3}0.096{col 54}{space 4}-.0168551{col 67}{space 3} .2052895
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}. eststo: xtlogit claimHS selfenum, fe
{txt}note: multiple positive outcomes within groups encountered.
note: 3 groups (27 obs) dropped because of all positive or
      all negative outcomes.
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-1428.8894}  
Iteration 1:{space 3}log likelihood = {res:-1428.8074}  
Iteration 2:{space 3}log likelihood = {res:-1428.8074}  
{res}
{txt}Conditional fixed-effects logistic regression{col 49}Number of obs{col 67}={col 69}{res}     3,017
{txt}Group variable: {res}psgc{col 49}{txt}Number of groups{col 67}={col 69}{res}       297

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         7
{txt}{col 63}avg{col 67}={col 69}{res}      10.2
{txt}{col 63}max{col 67}={col 69}{res}        14

{txt}{col 49}LR chi2({res}1{txt}){col 67}={col 70}{res}     0.65
{txt}Log likelihood  = {res}-1428.8074{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.4197

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     claimHS{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}selfenum {c |}{col 14}{res}{space 2}-.0624918{col 26}{space 2} .0774527{col 37}{space 1}   -0.81{col 46}{space 3}0.420{col 54}{space 4}-.2142963{col 67}{space 3} .0893128
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. eststo: xtlogit claimHS selfenum crowd selfenum_crowd male age income, fe
{txt}note: multiple positive outcomes within groups encountered.
note: 3 groups (27 obs) dropped because of all positive or
      all negative outcomes.
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-1153.5701}  
Iteration 1:{space 3}log likelihood = {res:-1151.7364}  
Iteration 2:{space 3}log likelihood = {res:-1151.7353}  
Iteration 3:{space 3}log likelihood = {res:-1151.7353}  
{res}
{txt}Conditional fixed-effects logistic regression{col 49}Number of obs{col 67}={col 69}{res}     2,963
{txt}Group variable: {res}psgc{col 49}{txt}Number of groups{col 67}={col 69}{res}       297

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}         7
{txt}{col 63}avg{col 67}={col 69}{res}      10.0
{txt}{col 63}max{col 67}={col 69}{res}        14

{txt}{col 49}LR chi2({res}6{txt}){col 67}={col 70}{res}   480.84
{txt}Log likelihood  = {res}-1151.7353{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       claimHS{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}selfenum {c |}{col 16}{res}{space 2}-.0431114{col 28}{space 2} .1003143{col 39}{space 1}   -0.43{col 48}{space 3}0.667{col 56}{space 4}-.2397239{col 69}{space 3}  .153501
{txt}{space 9}crowd {c |}{col 16}{res}{space 2}-.2699437{col 28}{space 2} .1493536{col 39}{space 1}   -1.81{col 48}{space 3}0.071{col 56}{space 4}-.5626713{col 69}{space 3}  .022784
{txt}selfenum_crowd {c |}{col 16}{res}{space 2}-.1525697{col 28}{space 2} .2073595{col 39}{space 1}   -0.74{col 48}{space 3}0.462{col 56}{space 4}-.5589869{col 69}{space 3} .2538476
{txt}{space 10}male {c |}{col 16}{res}{space 2} .0494368{col 28}{space 2} .0961977{col 39}{space 1}    0.51{col 48}{space 3}0.607{col 56}{space 4}-.1391072{col 69}{space 3} .2379808
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0518437{col 28}{space 2} .0032511{col 39}{space 1}  -15.95{col 48}{space 3}0.000{col 56}{space 4}-.0582156{col 69}{space 3}-.0454717
{txt}{space 8}income {c |}{col 16}{res}{space 2} .0000782{col 28}{space 2} 8.11e-06{col 39}{space 1}    9.64{col 48}{space 3}0.000{col 56}{space 4} .0000623{col 69}{space 3} .0000941
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}. *esttab using "selfvdirect_plac.tex", se star(* .10 ** .05 *** .01) nodep order(selfenum crowd) mtitle("" "" "" "" "") label title(Did you graduate high school?\label{c -(}tab:selfvdirectplac{c )-}) note(Logistic regression with barangay-clustered SE.) replace
. 
. 
. **Cognitive Load on Enumerators
. 
. *Generate indicator variables for each enumerator
. tab enumerator, gen(enumid)

 {txt}enumerator {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Agnes {c |}{res}        266        5.91        5.91
{txt}      Alvin {c |}{res}        155        3.44        9.35
{txt}  Ana Marie {c |}{res}        370        8.22       17.57
{txt}  Christine {c |}{res}        386        8.57       26.14
{txt}   Daizylyn {c |}{res}        311        6.91       33.04
{txt}     Darwin {c |}{res}        594       13.19       46.24
{txt}      Diana {c |}{res}        275        6.11       52.34
{txt}        Mae {c |}{res}         97        2.15       54.50
{txt}    Maricel {c |}{res}        242        5.37       59.87
{txt} Mark Lloyd {c |}{res}        370        8.22       68.09
{txt}   Mary Ann {c |}{res}        387        8.59       76.68
{txt}     Patria {c |}{res}         93        2.07       78.75
{txt}     Remely {c |}{res}        233        5.17       83.92
{txt}       Rita {c |}{res}        319        7.08       91.01
{txt}       Robe {c |}{res}        293        6.51       97.51
{txt}     Shiera {c |}{res}        112        2.49      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      4,503      100.00
{txt}
{com}. foreach enumerator in enumid2 enumid3 enumid4 enumid5 enumid6 enumid7 enumid8 enumid9 enumid10 enumid11 enumid12 enumid13 enumid14 enumid15 enumid16 {c -(}
{txt}  2{com}.         label variable `enumerator'
{txt}  3{com}.         {c )-}
{txt}
{com}. 
. *TABLE 5 (appendix): Survey Method and Enumerator Effects
. eststo clear
{txt}
{com}. eststo: logit reportdir crowd enumid2 enumid3 enumid4 enumid5 enumid6 enumid7 enumid8 enumid9 enumid10 enumid11 enumid12 enumid13 enumid14 enumid15 enumid16, cl(psgc)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-748.67799}  
Iteration 1:{space 3}log pseudolikelihood = {res:-735.56724}  
Iteration 2:{space 3}log pseudolikelihood = {res:-735.52877}  
Iteration 3:{space 3}log pseudolikelihood = {res:-735.52876}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,121
{txt}{col 49}Wald chi2({res}16{txt}){col 67}= {res}     22.31
{txt}{col 49}Prob > chi2{col 67}= {res}    0.1334
{txt}Log pseudolikelihood = {res}-735.52876{txt}{col 49}Pseudo R2{col 67}= {res}    0.0176

{txt}{ralign 78:(Std. Err. adjusted for {res:294} clusters in psgc)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   reportdir{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}crowd {c |}{col 14}{res}{space 2}-.1419803{col 26}{space 2} .1651194{col 37}{space 1}   -0.86{col 46}{space 3}0.390{col 54}{space 4}-.4656084{col 67}{space 3} .1816478
{txt}{space 5}enumid2 {c |}{col 14}{res}{space 2}-.2074881{col 26}{space 2} .4549396{col 37}{space 1}   -0.46{col 46}{space 3}0.648{col 54}{space 4}-1.099153{col 67}{space 3} .6841773
{txt}{space 5}enumid3 {c |}{col 14}{res}{space 2}-.2699097{col 26}{space 2} .3844722{col 37}{space 1}   -0.70{col 46}{space 3}0.483{col 54}{space 4}-1.023461{col 67}{space 3}  .483642
{txt}{space 5}enumid4 {c |}{col 14}{res}{space 2}-.2634431{col 26}{space 2} .3436992{col 37}{space 1}   -0.77{col 46}{space 3}0.443{col 54}{space 4}-.9370812{col 67}{space 3} .4101949
{txt}{space 5}enumid5 {c |}{col 14}{res}{space 2}-.1307152{col 26}{space 2} .4085597{col 37}{space 1}   -0.32{col 46}{space 3}0.749{col 54}{space 4}-.9314775{col 67}{space 3} .6700471
{txt}{space 5}enumid6 {c |}{col 14}{res}{space 2} .4788186{col 26}{space 2} .3530703{col 37}{space 1}    1.36{col 46}{space 3}0.175{col 54}{space 4}-.2131865{col 67}{space 3} 1.170824
{txt}{space 5}enumid7 {c |}{col 14}{res}{space 2} -.429208{col 26}{space 2} .4014486{col 37}{space 1}   -1.07{col 46}{space 3}0.285{col 54}{space 4}-1.216033{col 67}{space 3} .3576167
{txt}{space 5}enumid8 {c |}{col 14}{res}{space 2}-.2485117{col 26}{space 2} .4166606{col 37}{space 1}   -0.60{col 46}{space 3}0.551{col 54}{space 4}-1.065152{col 67}{space 3} .5681281
{txt}{space 5}enumid9 {c |}{col 14}{res}{space 2} .2289594{col 26}{space 2} .4652436{col 37}{space 1}    0.49{col 46}{space 3}0.623{col 54}{space 4}-.6829013{col 67}{space 3}  1.14082
{txt}{space 4}enumid10 {c |}{col 14}{res}{space 2}-.2394902{col 26}{space 2} .3426503{col 37}{space 1}   -0.70{col 46}{space 3}0.485{col 54}{space 4}-.9110724{col 67}{space 3} .4320921
{txt}{space 4}enumid11 {c |}{col 14}{res}{space 2} .4269895{col 26}{space 2} .3665133{col 37}{space 1}    1.17{col 46}{space 3}0.244{col 54}{space 4}-.2913633{col 67}{space 3} 1.145342
{txt}{space 4}enumid12 {c |}{col 14}{res}{space 2} .2482018{col 26}{space 2} .8085058{col 37}{space 1}    0.31{col 46}{space 3}0.759{col 54}{space 4} -1.33644{col 67}{space 3} 1.832844
{txt}{space 4}enumid13 {c |}{col 14}{res}{space 2} .3705275{col 26}{space 2} .4290854{col 37}{space 1}    0.86{col 46}{space 3}0.388{col 54}{space 4}-.4704644{col 67}{space 3} 1.211519
{txt}{space 4}enumid14 {c |}{col 14}{res}{space 2} .0257055{col 26}{space 2} .3516453{col 37}{space 1}    0.07{col 46}{space 3}0.942{col 54}{space 4}-.6635066{col 67}{space 3} .7149177
{txt}{space 4}enumid15 {c |}{col 14}{res}{space 2}-.3800795{col 26}{space 2} .3675294{col 37}{space 1}   -1.03{col 46}{space 3}0.301{col 54}{space 4}-1.100424{col 67}{space 3} .3402648
{txt}{space 4}enumid16 {c |}{col 14}{res}{space 2}-.0090543{col 26}{space 2} .6385702{col 37}{space 1}   -0.01{col 46}{space 3}0.989{col 54}{space 4}-1.260629{col 67}{space 3}  1.24252
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5167757{col 26}{space 2} .2990688{col 37}{space 1}    1.73{col 46}{space 3}0.084{col 54}{space 4}-.0693884{col 67}{space 3}  1.10294
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}. eststo: logit reportself crowd enumid2 enumid3 enumid4 enumid5 enumid6 enumid7 enumid8 enumid9 enumid10 enumid11 enumid12 enumid13 enumid14 enumid15 enumid16, cl(psgc)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-733.64105}  
Iteration 1:{space 3}log pseudolikelihood = {res:-708.15144}  
Iteration 2:{space 3}log pseudolikelihood = {res:-707.76702}  
Iteration 3:{space 3}log pseudolikelihood = {res:-707.76603}  
Iteration 4:{space 3}log pseudolikelihood = {res:-707.76603}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,096
{txt}{col 49}Wald chi2({res}16{txt}){col 67}= {res}     46.75
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0001
{txt}Log pseudolikelihood = {res}-707.76603{txt}{col 49}Pseudo R2{col 67}= {res}    0.0353

{txt}{ralign 78:(Std. Err. adjusted for {res:294} clusters in psgc)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}  reportself{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}crowd {c |}{col 14}{res}{space 2}-.3675025{col 26}{space 2}   .17526{col 37}{space 1}   -2.10{col 46}{space 3}0.036{col 54}{space 4}-.7110059{col 67}{space 3}-.0239992
{txt}{space 5}enumid2 {c |}{col 14}{res}{space 2} .1829915{col 26}{space 2} .4943229{col 37}{space 1}    0.37{col 46}{space 3}0.711{col 54}{space 4}-.7858637{col 67}{space 3} 1.151847
{txt}{space 5}enumid3 {c |}{col 14}{res}{space 2}-.7193851{col 26}{space 2} .3388545{col 37}{space 1}   -2.12{col 46}{space 3}0.034{col 54}{space 4}-1.383528{col 67}{space 3}-.0552424
{txt}{space 5}enumid4 {c |}{col 14}{res}{space 2}  .715248{col 26}{space 2} .3373993{col 37}{space 1}    2.12{col 46}{space 3}0.034{col 54}{space 4} .0539575{col 67}{space 3} 1.376539
{txt}{space 5}enumid5 {c |}{col 14}{res}{space 2}-.5058196{col 26}{space 2} .3677699{col 37}{space 1}   -1.38{col 46}{space 3}0.169{col 54}{space 4}-1.226635{col 67}{space 3} .2149962
{txt}{space 5}enumid6 {c |}{col 14}{res}{space 2} .1822462{col 26}{space 2} .3047171{col 37}{space 1}    0.60{col 46}{space 3}0.550{col 54}{space 4}-.4149882{col 67}{space 3} .7794807
{txt}{space 5}enumid7 {c |}{col 14}{res}{space 2} .3480924{col 26}{space 2} .3555616{col 37}{space 1}    0.98{col 46}{space 3}0.328{col 54}{space 4}-.3487955{col 67}{space 3}  1.04498
{txt}{space 5}enumid8 {c |}{col 14}{res}{space 2}-.4436264{col 26}{space 2} .4778675{col 37}{space 1}   -0.93{col 46}{space 3}0.353{col 54}{space 4}-1.380229{col 67}{space 3} .4929767
{txt}{space 5}enumid9 {c |}{col 14}{res}{space 2}-.2604498{col 26}{space 2} .3563719{col 37}{space 1}   -0.73{col 46}{space 3}0.465{col 54}{space 4}-.9589259{col 67}{space 3} .4380262
{txt}{space 4}enumid10 {c |}{col 14}{res}{space 2}-.1646125{col 26}{space 2} .3167803{col 37}{space 1}   -0.52{col 46}{space 3}0.603{col 54}{space 4}-.7854904{col 67}{space 3} .4562654
{txt}{space 4}enumid11 {c |}{col 14}{res}{space 2}-.1449607{col 26}{space 2} .3391806{col 37}{space 1}   -0.43{col 46}{space 3}0.669{col 54}{space 4}-.8097426{col 67}{space 3} .5198211
{txt}{space 4}enumid12 {c |}{col 14}{res}{space 2}-.6286082{col 26}{space 2} .4809828{col 37}{space 1}   -1.31{col 46}{space 3}0.191{col 54}{space 4}-1.571317{col 67}{space 3} .3141007
{txt}{space 4}enumid13 {c |}{col 14}{res}{space 2}  .176614{col 26}{space 2} .4067546{col 37}{space 1}    0.43{col 46}{space 3}0.664{col 54}{space 4}-.6206103{col 67}{space 3} .9738383
{txt}{space 4}enumid14 {c |}{col 14}{res}{space 2}-.4890463{col 26}{space 2} .3611255{col 37}{space 1}   -1.35{col 46}{space 3}0.176{col 54}{space 4}-1.196839{col 67}{space 3} .2187467
{txt}{space 4}enumid15 {c |}{col 14}{res}{space 2} .0326709{col 26}{space 2} .3547766{col 37}{space 1}    0.09{col 46}{space 3}0.927{col 54}{space 4}-.6626784{col 67}{space 3} .7280203
{txt}{space 4}enumid16 {c |}{col 14}{res}{space 2} 1.494684{col 26}{space 2} .6021072{col 37}{space 1}    2.48{col 46}{space 3}0.013{col 54}{space 4} .3145757{col 67}{space 3} 2.674792
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5771578{col 26}{space 2} .2592152{col 37}{space 1}    2.23{col 46}{space 3}0.026{col 54}{space 4} .0691053{col 67}{space 3}  1.08521
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. eststo: rrlogit reportrr crowd enumid2 enumid3 enumid4 enumid5 enumid6 enumid7 enumid8 enumid9 enumid10 enumid11 enumid12 enumid13 enumid14 enumid15 enumid16, pyes(0.5) cl(psgc)

{txt}Fitting constant-only model:

Iteration 0:{col 16}log pseudolikelihood = {res}-729.57853{txt}  
Iteration 1:{col 16}log pseudolikelihood = {res}-716.38259{txt}  
Iteration 2:{col 16}log pseudolikelihood = {res}-716.32731{txt}  
Iteration 3:{col 16}log pseudolikelihood = {res}-716.32729{txt}  

Fitting full model:

Iteration 0:{col 16}log pseudolikelihood = {res}-716.32729{txt}  
Iteration 1:{col 16}log pseudolikelihood = {res}-699.33392{txt}  
Iteration 2:{col 16}log pseudolikelihood = {res}-695.40513{txt}  (not concave)
Iteration 3:{col 16}log pseudolikelihood = {res}-689.89178{txt}  
Iteration 4:{col 16}log pseudolikelihood = {res}-688.28604{txt}  
Iteration 5:{col 16}log pseudolikelihood = {res}-688.02563{txt}  
Iteration 6:{col 16}log pseudolikelihood = {res}-687.99172{txt}  
Iteration 7:{col 16}log pseudolikelihood = {res}-687.98627{txt}  
Iteration 8:{col 16}log pseudolikelihood = {res}-687.98577{txt}  
Iteration 9:{col 16}log pseudolikelihood = {res}-687.98576{txt}  
{res}
{txt}Randomized response logistic regression{col 49}Number of obs     ={res}{col 70}     1146
{txt}{col 49}Nonzero outcomes  ={res}{col 70}      782
{txt}P(non-negated question) =  {res}1{txt}{col 49}Zero outcomes     ={res}{col 70}      364
{txt}P(surrogate "yes")      =  {res}0.5{txt}{col 49}Wald chi2({res}16{txt}){col 67}={res}{col 70}    25.64
{txt}P(surrogate "no")       =  {res}0{txt}{col 49}Prob > chi2{col 67}={res}{col 70}   0.0592
{txt}Log pseudolikelihood = {res}-687.98576{txt}{col 49}Pseudo R2{col 67}={res}{col 70}   0.0396

{txt}{ralign 78:(Std. Err. adjusted for {res:302} clusters in psgc)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    reportrr{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}crowd {c |}{col 14}{res}{space 2}-.7403637{col 26}{space 2}  .444647{col 37}{space 1}   -1.67{col 46}{space 3}0.096{col 54}{space 4}-1.611856{col 67}{space 3} .1311284
{txt}{space 5}enumid2 {c |}{col 14}{res}{space 2} .6144016{col 26}{space 2}  .800358{col 37}{space 1}    0.77{col 46}{space 3}0.443{col 54}{space 4}-.9542712{col 67}{space 3} 2.183075
{txt}{space 5}enumid3 {c |}{col 14}{res}{space 2} -1.22565{col 26}{space 2} .6500239{col 37}{space 1}   -1.89{col 46}{space 3}0.059{col 54}{space 4}-2.499673{col 67}{space 3} .0483736
{txt}{space 5}enumid4 {c |}{col 14}{res}{space 2}-.4291889{col 26}{space 2} .5634547{col 37}{space 1}   -0.76{col 46}{space 3}0.446{col 54}{space 4} -1.53354{col 67}{space 3} .6751621
{txt}{space 5}enumid5 {c |}{col 14}{res}{space 2}-1.152409{col 26}{space 2} .8136897{col 37}{space 1}   -1.42{col 46}{space 3}0.157{col 54}{space 4}-2.747211{col 67}{space 3} .4423934
{txt}{space 5}enumid6 {c |}{col 14}{res}{space 2} -.305678{col 26}{space 2} .5120018{col 37}{space 1}   -0.60{col 46}{space 3}0.550{col 54}{space 4}-1.309183{col 67}{space 3} .6978272
{txt}{space 5}enumid7 {c |}{col 14}{res}{space 2}-4.726795{col 26}{space 2}  9.44446{col 37}{space 1}   -0.50{col 46}{space 3}0.617{col 54}{space 4} -23.2376{col 67}{space 3} 13.78401
{txt}{space 5}enumid8 {c |}{col 14}{res}{space 2}-2.564153{col 26}{space 2} 2.104851{col 37}{space 1}   -1.22{col 46}{space 3}0.223{col 54}{space 4}-6.689585{col 67}{space 3} 1.561278
{txt}{space 5}enumid9 {c |}{col 14}{res}{space 2}-.8748801{col 26}{space 2} .6987377{col 37}{space 1}   -1.25{col 46}{space 3}0.211{col 54}{space 4}-2.244381{col 67}{space 3} .4946207
{txt}{space 4}enumid10 {c |}{col 14}{res}{space 2}-3.073937{col 26}{space 2} 1.712031{col 37}{space 1}   -1.80{col 46}{space 3}0.073{col 54}{space 4}-6.429456{col 67}{space 3} .2815828
{txt}{space 4}enumid11 {c |}{col 14}{res}{space 2} -1.70561{col 26}{space 2} .7812264{col 37}{space 1}   -2.18{col 46}{space 3}0.029{col 54}{space 4}-3.236785{col 67}{space 3}-.1744341
{txt}{space 4}enumid12 {c |}{col 14}{res}{space 2}-.8340688{col 26}{space 2} 1.411234{col 37}{space 1}   -0.59{col 46}{space 3}0.555{col 54}{space 4}-3.600037{col 67}{space 3} 1.931899
{txt}{space 4}enumid13 {c |}{col 14}{res}{space 2}-1.147078{col 26}{space 2} .7633223{col 37}{space 1}   -1.50{col 46}{space 3}0.133{col 54}{space 4}-2.643163{col 67}{space 3} .3490058
{txt}{space 4}enumid14 {c |}{col 14}{res}{space 2}-1.298225{col 26}{space 2} .6206666{col 37}{space 1}   -2.09{col 46}{space 3}0.036{col 54}{space 4}-2.514709{col 67}{space 3} -.081741
{txt}{space 4}enumid15 {c |}{col 14}{res}{space 2}-.3039628{col 26}{space 2} .5486286{col 37}{space 1}   -0.55{col 46}{space 3}0.580{col 54}{space 4}-1.379255{col 67}{space 3} .7713294
{txt}{space 4}enumid16 {c |}{col 14}{res}{space 2} .9105427{col 26}{space 2} .9384374{col 37}{space 1}    0.97{col 46}{space 3}0.332{col 54}{space 4}-.9287608{col 67}{space 3} 2.749846
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .5717234{col 26}{space 2} .4372064{col 37}{space 1}    1.31{col 46}{space 3}0.191{col 54}{space 4}-.2851854{col 67}{space 3} 1.428632
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}. eststo: logit hsdir crowd enumid2 enumid3 enumid4 enumid5 enumid6 enumid7 enumid8 enumid9 enumid10 enumid11 enumid12 enumid13 enumid14 enumid15 enumid16, cl(psgc)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -1087.885}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1068.0368}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1068.0056}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1068.0056}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,572
{txt}{col 49}Wald chi2({res}16{txt}){col 67}= {res}     40.90
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0006
{txt}Log pseudolikelihood = {res}-1068.0056{txt}{col 49}Pseudo R2{col 67}= {res}    0.0183

{txt}{ralign 78:(Std. Err. adjusted for {res:300} clusters in psgc)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    hsdirect{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}crowd {c |}{col 14}{res}{space 2}-.5667704{col 26}{space 2}  .140866{col 37}{space 1}   -4.02{col 46}{space 3}0.000{col 54}{space 4}-.8428626{col 67}{space 3}-.2906782
{txt}{space 5}enumid2 {c |}{col 14}{res}{space 2}-.0872408{col 26}{space 2} .3117238{col 37}{space 1}   -0.28{col 46}{space 3}0.780{col 54}{space 4}-.6982083{col 67}{space 3} .5237267
{txt}{space 5}enumid3 {c |}{col 14}{res}{space 2} .2403384{col 26}{space 2} .2647664{col 37}{space 1}    0.91{col 46}{space 3}0.364{col 54}{space 4}-.2785942{col 67}{space 3}  .759271
{txt}{space 5}enumid4 {c |}{col 14}{res}{space 2}-.0673529{col 26}{space 2} .2443227{col 37}{space 1}   -0.28{col 46}{space 3}0.783{col 54}{space 4}-.5462166{col 67}{space 3} .4115107
{txt}{space 5}enumid5 {c |}{col 14}{res}{space 2}  .418442{col 26}{space 2} .2818268{col 37}{space 1}    1.48{col 46}{space 3}0.138{col 54}{space 4}-.1339283{col 67}{space 3} .9708123
{txt}{space 5}enumid6 {c |}{col 14}{res}{space 2} .0324922{col 26}{space 2} .2326863{col 37}{space 1}    0.14{col 46}{space 3}0.889{col 54}{space 4}-.4235645{col 67}{space 3} .4885489
{txt}{space 5}enumid7 {c |}{col 14}{res}{space 2}  .151569{col 26}{space 2} .2942221{col 37}{space 1}    0.52{col 46}{space 3}0.606{col 54}{space 4}-.4250957{col 67}{space 3} .7282337
{txt}{space 5}enumid8 {c |}{col 14}{res}{space 2} -.070538{col 26}{space 2} .3817732{col 37}{space 1}   -0.18{col 46}{space 3}0.853{col 54}{space 4}-.8187997{col 67}{space 3} .6777237
{txt}{space 5}enumid9 {c |}{col 14}{res}{space 2}-.2009552{col 26}{space 2} .3192296{col 37}{space 1}   -0.63{col 46}{space 3}0.529{col 54}{space 4}-.8266338{col 67}{space 3} .4247233
{txt}{space 4}enumid10 {c |}{col 14}{res}{space 2}-.3049924{col 26}{space 2}  .253027{col 37}{space 1}   -1.21{col 46}{space 3}0.228{col 54}{space 4}-.8009163{col 67}{space 3} .1909315
{txt}{space 4}enumid11 {c |}{col 14}{res}{space 2} .8422053{col 26}{space 2}  .280483{col 37}{space 1}    3.00{col 46}{space 3}0.003{col 54}{space 4} .2924688{col 67}{space 3} 1.391942
{txt}{space 4}enumid12 {c |}{col 14}{res}{space 2} .7252549{col 26}{space 2} .4007859{col 37}{space 1}    1.81{col 46}{space 3}0.070{col 54}{space 4} -.060271{col 67}{space 3} 1.510781
{txt}{space 4}enumid13 {c |}{col 14}{res}{space 2}-.1246052{col 26}{space 2} .2940786{col 37}{space 1}   -0.42{col 46}{space 3}0.672{col 54}{space 4}-.7009887{col 67}{space 3} .4517783
{txt}{space 4}enumid14 {c |}{col 14}{res}{space 2}-.0260286{col 26}{space 2} .2452093{col 37}{space 1}   -0.11{col 46}{space 3}0.915{col 54}{space 4}  -.50663{col 67}{space 3} .4545727
{txt}{space 4}enumid15 {c |}{col 14}{res}{space 2} .1060341{col 26}{space 2} .3054637{col 37}{space 1}    0.35{col 46}{space 3}0.728{col 54}{space 4}-.4926639{col 67}{space 3}  .704732
{txt}{space 4}enumid16 {c |}{col 14}{res}{space 2} .2930157{col 26}{space 2} .4453502{col 37}{space 1}    0.66{col 46}{space 3}0.511{col 54}{space 4}-.5798546{col 67}{space 3} 1.165886
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .1246052{col 26}{space 2} .1941836{col 37}{space 1}    0.64{col 46}{space 3}0.521{col 54}{space 4}-.2559877{col 67}{space 3}  .505198
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}. eststo: logit hsself crowd enumid2 enumid3 enumid4 enumid5 enumid6 enumid7 enumid8 enumid9 enumid10 enumid11 enumid12 enumid13 enumid14 enumid15 enumid16, cl(psgc)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1019.3219}  
Iteration 1:{space 3}log pseudolikelihood = {res: -1003.496}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1003.4812}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1003.4812}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,472
{txt}{col 49}Wald chi2({res}16{txt}){col 67}= {res}     31.65
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0111
{txt}Log pseudolikelihood = {res}-1003.4812{txt}{col 49}Pseudo R2{col 67}= {res}    0.0155

{txt}{ralign 78:(Std. Err. adjusted for {res:300} clusters in psgc)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}      hsself{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}crowd {c |}{col 14}{res}{space 2}-.6491849{col 26}{space 2} .1483264{col 37}{space 1}   -4.38{col 46}{space 3}0.000{col 54}{space 4}-.9398993{col 67}{space 3}-.3584706
{txt}{space 5}enumid2 {c |}{col 14}{res}{space 2} .1919329{col 26}{space 2}  .441384{col 37}{space 1}    0.43{col 46}{space 3}0.664{col 54}{space 4}-.6731639{col 67}{space 3}  1.05703
{txt}{space 5}enumid3 {c |}{col 14}{res}{space 2}-.0079208{col 26}{space 2} .3202954{col 37}{space 1}   -0.02{col 46}{space 3}0.980{col 54}{space 4}-.6356884{col 67}{space 3} .6198467
{txt}{space 5}enumid4 {c |}{col 14}{res}{space 2} .2831465{col 26}{space 2} .3196185{col 37}{space 1}    0.89{col 46}{space 3}0.376{col 54}{space 4}-.3432942{col 67}{space 3} .9095873
{txt}{space 5}enumid5 {c |}{col 14}{res}{space 2} .3812736{col 26}{space 2} .3304834{col 37}{space 1}    1.15{col 46}{space 3}0.249{col 54}{space 4}-.2664619{col 67}{space 3} 1.029009
{txt}{space 5}enumid6 {c |}{col 14}{res}{space 2}-.1175029{col 26}{space 2} .2822761{col 37}{space 1}   -0.42{col 46}{space 3}0.677{col 54}{space 4}-.6707539{col 67}{space 3}  .435748
{txt}{space 5}enumid7 {c |}{col 14}{res}{space 2} .8003855{col 26}{space 2} .3694842{col 37}{space 1}    2.17{col 46}{space 3}0.030{col 54}{space 4} .0762098{col 67}{space 3} 1.524561
{txt}{space 5}enumid8 {c |}{col 14}{res}{space 2} .0072852{col 26}{space 2} .4538664{col 37}{space 1}    0.02{col 46}{space 3}0.987{col 54}{space 4}-.8822766{col 67}{space 3} .8968469
{txt}{space 5}enumid9 {c |}{col 14}{res}{space 2} -.297927{col 26}{space 2}  .317564{col 37}{space 1}   -0.94{col 46}{space 3}0.348{col 54}{space 4}-.9203411{col 67}{space 3}  .324487
{txt}{space 4}enumid10 {c |}{col 14}{res}{space 2}  -.07766{col 26}{space 2} .2938359{col 37}{space 1}   -0.26{col 46}{space 3}0.792{col 54}{space 4}-.6535678{col 67}{space 3} .4982478
{txt}{space 4}enumid11 {c |}{col 14}{res}{space 2} .6165154{col 26}{space 2} .3064357{col 37}{space 1}    2.01{col 46}{space 3}0.044{col 54}{space 4} .0159125{col 67}{space 3} 1.217118
{txt}{space 4}enumid12 {c |}{col 14}{res}{space 2} .3261448{col 26}{space 2}  .384001{col 37}{space 1}    0.85{col 46}{space 3}0.396{col 54}{space 4}-.4264833{col 67}{space 3} 1.078773
{txt}{space 4}enumid13 {c |}{col 14}{res}{space 2} .0309558{col 26}{space 2} .3518964{col 37}{space 1}    0.09{col 46}{space 3}0.930{col 54}{space 4}-.6587485{col 67}{space 3} .7206601
{txt}{space 4}enumid14 {c |}{col 14}{res}{space 2}  .301878{col 26}{space 2} .3130367{col 37}{space 1}    0.96{col 46}{space 3}0.335{col 54}{space 4}-.3116627{col 67}{space 3} .9154186
{txt}{space 4}enumid15 {c |}{col 14}{res}{space 2}  -.05951{col 26}{space 2}  .339226{col 37}{space 1}   -0.18{col 46}{space 3}0.861{col 54}{space 4}-.7243807{col 67}{space 3} .6053608
{txt}{space 4}enumid16 {c |}{col 14}{res}{space 2} .3120509{col 26}{space 2} .3877131{col 37}{space 1}    0.80{col 46}{space 3}0.421{col 54}{space 4}-.4478528{col 67}{space 3} 1.071954
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0904051{col 26}{space 2} .2438613{col 37}{space 1}    0.37{col 46}{space 3}0.711{col 54}{space 4}-.3875543{col 67}{space 3} .5683644
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{com}. eststo: rrlogit hsrr crowd enumid2 enumid3 enumid4 enumid5 enumid6 enumid7 enumid8 enumid9 enumid10 enumid11 enumid12 enumid13 enumid14 enumid15 enumid16, pyes(0.5) cl(psgc)

{txt}Fitting constant-only model:

Iteration 0:{col 16}log pseudolikelihood = {res}-997.52108{txt}  
Iteration 1:{col 16}log pseudolikelihood = {res}-945.37841{txt}  
Iteration 2:{col 16}log pseudolikelihood = {res}-945.29155{txt}  
Iteration 3:{col 16}log pseudolikelihood = {res}-945.29139{txt}  
Iteration 4:{col 16}log pseudolikelihood = {res}-945.29139{txt}  

Fitting full model:

Iteration 0:{col 16}log pseudolikelihood = {res}-945.29139{txt}  
Iteration 1:{col 16}log pseudolikelihood = {res}-936.42102{txt}  
Iteration 2:{col 16}log pseudolikelihood = {res}-930.50493{txt}  
Iteration 3:{col 16}log pseudolikelihood = {res}-930.16846{txt}  
Iteration 4:{col 16}log pseudolikelihood = {res}-930.11261{txt}  
Iteration 5:{col 16}log pseudolikelihood = {res}-930.09966{txt}  
Iteration 6:{col 16}log pseudolikelihood = {res}-930.09604{txt}  
Iteration 7:{col 16}log pseudolikelihood = {res} -930.0951{txt}  
Iteration 8:{col 16}log pseudolikelihood = {res} -930.0949{txt}  
Iteration 9:{col 16}log pseudolikelihood = {res}-930.09486{txt}  
Iteration 10:{col 16}log pseudolikelihood = {res}-930.09485{txt}  
{res}
{txt}Randomized response logistic regression{col 49}Number of obs     ={res}{col 70}     1432
{txt}{col 49}Nonzero outcomes  ={res}{col 70}      899
{txt}P(non-negated question) =  {res}1{txt}{col 49}Zero outcomes     ={res}{col 70}      533
{txt}P(surrogate "yes")      =  {res}0.5{txt}{col 49}Wald chi2({res}16{txt}){col 67}={res}{col 70}    25.23
{txt}P(surrogate "no")       =  {res}0{txt}{col 49}Prob > chi2{col 67}={res}{col 70}   0.0658
{txt}Log pseudolikelihood = {res}-930.09485{txt}{col 49}Pseudo R2{col 67}={res}{col 70}   0.0161

{txt}{ralign 78:(Std. Err. adjusted for {res:302} clusters in psgc)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}        hsrr{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}crowd {c |}{col 14}{res}{space 2}-1.385317{col 26}{space 2}  .625436{col 37}{space 1}   -2.21{col 46}{space 3}0.027{col 54}{space 4}-2.611149{col 67}{space 3}-.1594846
{txt}{space 5}enumid2 {c |}{col 14}{res}{space 2}-.7429394{col 26}{space 2} .9596803{col 37}{space 1}   -0.77{col 46}{space 3}0.439{col 54}{space 4}-2.623878{col 67}{space 3} 1.137999
{txt}{space 5}enumid3 {c |}{col 14}{res}{space 2} -.565138{col 26}{space 2} .5759045{col 37}{space 1}   -0.98{col 46}{space 3}0.326{col 54}{space 4} -1.69389{col 67}{space 3} .5636141
{txt}{space 5}enumid4 {c |}{col 14}{res}{space 2}-1.270496{col 26}{space 2}  .662985{col 37}{space 1}   -1.92{col 46}{space 3}0.055{col 54}{space 4}-2.569923{col 67}{space 3} .0289308
{txt}{space 5}enumid5 {c |}{col 14}{res}{space 2}-.4192528{col 26}{space 2} .6102447{col 37}{space 1}   -0.69{col 46}{space 3}0.492{col 54}{space 4} -1.61531{col 67}{space 3} .7768048
{txt}{space 5}enumid6 {c |}{col 14}{res}{space 2}-1.711497{col 26}{space 2} .7164125{col 37}{space 1}   -2.39{col 46}{space 3}0.017{col 54}{space 4}-3.115639{col 67}{space 3}-.3073538
{txt}{space 5}enumid7 {c |}{col 14}{res}{space 2}-.4255772{col 26}{space 2} .6790505{col 37}{space 1}   -0.63{col 46}{space 3}0.531{col 54}{space 4}-1.756492{col 67}{space 3} .9053373
{txt}{space 5}enumid8 {c |}{col 14}{res}{space 2}-13.39982{col 26}{space 2}  5.02787{col 37}{space 1}   -2.67{col 46}{space 3}0.008{col 54}{space 4}-23.25426{col 67}{space 3}-3.545372
{txt}{space 5}enumid9 {c |}{col 14}{res}{space 2}-.2488873{col 26}{space 2}  .671239{col 37}{space 1}   -0.37{col 46}{space 3}0.711{col 54}{space 4}-1.564492{col 67}{space 3} 1.066717
{txt}{space 4}enumid10 {c |}{col 14}{res}{space 2}-2.002712{col 26}{space 2} 1.028167{col 37}{space 1}   -1.95{col 46}{space 3}0.051{col 54}{space 4}-4.017881{col 67}{space 3} .0124577
{txt}{space 4}enumid11 {c |}{col 14}{res}{space 2}-.7462312{col 26}{space 2} .7571217{col 37}{space 1}   -0.99{col 46}{space 3}0.324{col 54}{space 4}-2.230162{col 67}{space 3} .7377001
{txt}{space 4}enumid12 {c |}{col 14}{res}{space 2}-2.285504{col 26}{space 2} 2.231063{col 37}{space 1}   -1.02{col 46}{space 3}0.306{col 54}{space 4}-6.658308{col 67}{space 3} 2.087299
{txt}{space 4}enumid13 {c |}{col 14}{res}{space 2}-2.354266{col 26}{space 2} 1.424982{col 37}{space 1}   -1.65{col 46}{space 3}0.099{col 54}{space 4} -5.14718{col 67}{space 3} .4386478
{txt}{space 4}enumid14 {c |}{col 14}{res}{space 2}-.5973812{col 26}{space 2} .5842226{col 37}{space 1}   -1.02{col 46}{space 3}0.307{col 54}{space 4}-1.742436{col 67}{space 3}  .547674
{txt}{space 4}enumid15 {c |}{col 14}{res}{space 2}-.9284342{col 26}{space 2} .6587836{col 37}{space 1}   -1.41{col 46}{space 3}0.159{col 54}{space 4}-2.219626{col 67}{space 3} .3627579
{txt}{space 4}enumid16 {c |}{col 14}{res}{space 2} .2484475{col 26}{space 2} .8433552{col 37}{space 1}    0.29{col 46}{space 3}0.768{col 54}{space 4}-1.404498{col 67}{space 3} 1.901393
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .1366815{col 26}{space 2} .4671134{col 37}{space 1}    0.29{col 46}{space 3}0.770{col 54}{space 4} -.778844{col 67}{space 3} 1.052207
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est6{txt} stored)

{com}. *esttab using "enumfx.tex", se star(* .10 ** .05 *** .01) nodep order() mtitle("Report" "Report" "Report" "HS" "HS" "HS") label title(Survey Method and Enumerator Effects\label{c -(}tab:enumfx{c )-}) note(Logistic regression with barangay-clustered SE.) replace
. 
. **Heterogeneous Effects by Subgroup
. 
. *TABLE 6 (appendix): Heterogeneous Effects on Non-Response
. xtset psgc
{txt}{col 8}panel variable:  {res}psgc (unbalanced)
{txt}
{com}. eststo clear
{txt}
{com}. eststo: logit sensitivenr i.sensitivecat##c.age, cl(psgc)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2546.3564}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2532.0545}  
Iteration 2:{space 3}log pseudolikelihood = {res: -2532.002}  
Iteration 3:{space 3}log pseudolikelihood = {res: -2532.002}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,502
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}     32.04
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res} -2532.002{txt}{col 49}Pseudo R2{col 67}= {res}    0.0056

{txt}{ralign 84:(Std. Err. adjusted for {res:302} clusters in psgc)}
{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}       sensitivenr{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}sensitivecat {c |}
{space 13}Self  {c |}{col 20}{res}{space 2}-.2068316{col 32}{space 2} .2891973{col 43}{space 1}   -0.72{col 52}{space 3}0.474{col 60}{space 4}-.7736478{col 73}{space 3} .3599846
{txt}{space 6}Rand. Resp.  {c |}{col 20}{res}{space 2}-.7556722{col 32}{space 2} .2930172{col 43}{space 1}   -2.58{col 52}{space 3}0.010{col 60}{space 4}-1.329975{col 73}{space 3} -.181369
{txt}{space 18} {c |}
{space 15}age {c |}{col 20}{res}{space 2}-.0036104{col 32}{space 2} .0036318{col 43}{space 1}   -0.99{col 52}{space 3}0.320{col 60}{space 4}-.0107286{col 73}{space 3} .0035078
{txt}{space 18} {c |}
sensitivecat#c.age {c |}
{space 13}Self  {c |}{col 20}{res}{space 2} .0007747{col 32}{space 2}  .005677{col 43}{space 1}    0.14{col 52}{space 3}0.891{col 60}{space 4} -.010352{col 73}{space 3} .0119014
{txt}{space 6}Rand. Resp.  {c |}{col 20}{res}{space 2} .0066259{col 32}{space 2} .0058561{col 43}{space 1}    1.13{col 52}{space 3}0.258{col 60}{space 4}-.0048518{col 73}{space 3} .0181037
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2}-.7209942{col 32}{space 2}    .1876{col 43}{space 1}   -3.84{col 52}{space 3}0.000{col 60}{space 4}-1.088683{col 73}{space 3} -.353305
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}. eststo: logit sensitivenr i.sensitivecat##c.education, cl(psgc)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2544.0205}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2513.8831}  
Iteration 2:{space 3}log pseudolikelihood = {res: -2513.659}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2513.6589}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,494
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}     63.54
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-2513.6589{txt}{col 49}Pseudo R2{col 67}= {res}    0.0119

{txt}{ralign 90:(Std. Err. adjusted for {res:302} clusters in psgc)}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}             sensitivenr{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}sensitivecat {c |}
{space 19}Self  {c |}{col 26}{res}{space 2}-.2504088{col 38}{space 2} .1324756{col 49}{space 1}   -1.89{col 58}{space 3}0.059{col 66}{space 4}-.5100563{col 79}{space 3} .0092387
{txt}{space 12}Rand. Resp.  {c |}{col 26}{res}{space 2}-.4041073{col 38}{space 2} .1329318{col 49}{space 1}   -3.04{col 58}{space 3}0.002{col 66}{space 4}-.6646488{col 79}{space 3}-.1435659
{txt}{space 24} {c |}
{space 15}education {c |}{col 26}{res}{space 2} .1311947{col 38}{space 2} .0432357{col 49}{space 1}    3.03{col 58}{space 3}0.002{col 66}{space 4} .0464544{col 79}{space 3} .2159351
{txt}{space 24} {c |}
sensitivecat#c.education {c |}
{space 19}Self  {c |}{col 26}{res}{space 2}  .049116{col 38}{space 2}  .063132{col 49}{space 1}    0.78{col 58}{space 3}0.437{col 66}{space 4}-.0746205{col 79}{space 3} .1728524
{txt}{space 12}Rand. Resp.  {c |}{col 26}{res}{space 2}-.0215198{col 38}{space 2} .0615354{col 49}{space 1}   -0.35{col 58}{space 3}0.727{col 66}{space 4}-.1421269{col 79}{space 3} .0990873
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}-1.101773{col 38}{space 2} .0941165{col 49}{space 1}  -11.71{col 58}{space 3}0.000{col 66}{space 4}-1.286238{col 79}{space 3}-.9173082
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. eststo: logit placnr i.sensitivecat##c.age, cl(psgc)

{txt}note: 2.sensitivecat != 0 predicts failure perfectly
      2.sensitivecat dropped and 1474 obs not used

note: 2.sensitivecat#c.age omitted because of collinearity
{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-140.00713}  
Iteration 1:{space 3}log pseudolikelihood = {res:-135.52958}  
Iteration 2:{space 3}log pseudolikelihood = {res:-134.31421}  
Iteration 3:{space 3}log pseudolikelihood = {res:-134.29609}  
Iteration 4:{space 3}log pseudolikelihood = {res:-134.29608}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     3,028
{txt}{col 49}Wald chi2({res}3{txt}){col 67}= {res}     11.46
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0095
{txt}Log pseudolikelihood = {res}-134.29608{txt}{col 49}Pseudo R2{col 67}= {res}    0.0408

{txt}{ralign 84:(Std. Err. adjusted for {res:302} clusters in psgc)}
{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}            placnr{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}sensitivecat {c |}
{space 13}Self  {c |}{col 20}{res}{space 2}        0{col 32}{txt}  (empty)
{space 6}Rand. Resp.  {c |}{col 20}{res}{space 2}-.3319326{col 32}{space 2} 1.048714{col 43}{space 1}   -0.32{col 52}{space 3}0.752{col 60}{space 4}-2.387374{col 73}{space 3} 1.723509
{txt}{space 18} {c |}
{space 15}age {c |}{col 20}{res}{space 2}-.0546956{col 32}{space 2} .0199946{col 43}{space 1}   -2.74{col 52}{space 3}0.006{col 60}{space 4}-.0938843{col 73}{space 3}-.0155068
{txt}{space 18} {c |}
sensitivecat#c.age {c |}
{space 13}Self  {c |}{col 20}{res}{space 2}        0{col 32}{txt}  (empty)
{space 6}Rand. Resp.  {c |}{col 20}{res}{space 2} .0330435{col 32}{space 2} .0224138{col 43}{space 1}    1.47{col 52}{space 3}0.140{col 60}{space 4}-.0108867{col 73}{space 3} .0769738
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} -3.10966{col 32}{space 2} .8203084{col 43}{space 1}   -3.79{col 52}{space 3}0.000{col 60}{space 4}-4.717435{col 73}{space 3}-1.501885
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}. eststo: logit placnr i.sensitivecat##c.education, cl(psgc)

{txt}note: 2.sensitivecat != 0 predicts failure perfectly
      2.sensitivecat dropped and 1471 obs not used

note: 2.sensitivecat#c.education omitted because of collinearity
{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-139.96731}  
Iteration 1:{space 3}log pseudolikelihood = {res:-136.32152}  
Iteration 2:{space 3}log pseudolikelihood = {res:-135.71372}  
Iteration 3:{space 3}log pseudolikelihood = {res:-135.71212}  
Iteration 4:{space 3}log pseudolikelihood = {res:-135.71212}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     3,023
{txt}{col 49}Wald chi2({res}3{txt}){col 67}= {res}      6.22
{txt}{col 49}Prob > chi2{col 67}= {res}    0.1012
{txt}Log pseudolikelihood = {res}-135.71212{txt}{col 49}Pseudo R2{col 67}= {res}    0.0304

{txt}{ralign 90:(Std. Err. adjusted for {res:302} clusters in psgc)}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}                  placnr{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}sensitivecat {c |}
{space 19}Self  {c |}{col 26}{res}{space 2}        0{col 38}{txt}  (empty)
{space 12}Rand. Resp.  {c |}{col 26}{res}{space 2} 1.979863{col 38}{space 2}  .983646{col 49}{space 1}    2.01{col 58}{space 3}0.044{col 66}{space 4} .0519524{col 79}{space 3} 3.907774
{txt}{space 24} {c |}
{space 15}education {c |}{col 26}{res}{space 2} .3722127{col 38}{space 2} .3587457{col 49}{space 1}    1.04{col 58}{space 3}0.299{col 66}{space 4}-.3309158{col 79}{space 3} 1.075341
{txt}{space 24} {c |}
sensitivecat#c.education {c |}
{space 19}Self  {c |}{col 26}{res}{space 2}        0{col 38}{txt}  (empty)
{space 12}Rand. Resp.  {c |}{col 26}{res}{space 2}-.5842573{col 38}{space 2} .4275428{col 49}{space 1}   -1.37{col 58}{space 3}0.172{col 66}{space 4}-1.422226{col 79}{space 3} .2537113
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}-6.123148{col 38}{space 2}  .903399{col 49}{space 1}   -6.78{col 58}{space 3}0.000{col 66}{space 4}-7.893777{col 79}{space 3}-4.352519
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}. *esttab using "nonresp_het.tex", se star(* .10 ** .05 *** .01) nodep mtitle("Sensitive" "Sensitive" "Placebo" "Placebo") label title(Heterogeneous Effects on Non-Response\label{c -(}tab:nonresphet{c )-}) note(Logistic regression with barangay-clustered SE.) replace
. 
. *TABLE 7 (appendix): Heterogeneous Effects on Willingness to Report
. xtset psgc
{txt}{col 8}panel variable:  {res}psgc (unbalanced)
{txt}
{com}. eststo clear
{txt}
{com}. eststo: logit reportNPA selfenum##c.age, cl(psgc)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1482.3323}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1449.7855}  
Iteration 2:{space 3}log pseudolikelihood = {res: -1449.725}  
Iteration 3:{space 3}log pseudolikelihood = {res: -1449.725}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     2,217
{txt}{col 49}Wald chi2({res}3{txt}){col 67}= {res}     60.95
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res} -1449.725{txt}{col 49}Pseudo R2{col 67}= {res}    0.0220

{txt}{ralign 80:(Std. Err. adjusted for {res:300} clusters in psgc)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}     reportNPA{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}1.selfenum {c |}{col 16}{res}{space 2}-.1217404{col 28}{space 2} .3050063{col 39}{space 1}   -0.40{col 48}{space 3}0.690{col 56}{space 4}-.7195417{col 69}{space 3} .4760609
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0239425{col 28}{space 2}   .00415{col 39}{space 1}   -5.77{col 48}{space 3}0.000{col 56}{space 4}-.0320763{col 69}{space 3}-.0158087
{txt}{space 14} {c |}
selfenum#c.age {c |}
{space 12}1  {c |}{col 16}{res}{space 2} .0020532{col 28}{space 2} .0056882{col 39}{space 1}    0.36{col 48}{space 3}0.718{col 56}{space 4}-.0090955{col 69}{space 3} .0132019
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2} 1.636721{col 28}{space 2} .2187138{col 39}{space 1}    7.48{col 48}{space 3}0.000{col 56}{space 4} 1.208049{col 69}{space 3} 2.065392
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}. eststo: logit reportNPA selfenum##c.education, cl(psgc)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1477.5727}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1453.3944}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1453.3328}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1453.3328}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     2,211
{txt}{col 49}Wald chi2({res}3{txt}){col 67}= {res}     45.21
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-1453.3328{txt}{col 49}Pseudo R2{col 67}= {res}    0.0164

{txt}{ralign 86:(Std. Err. adjusted for {res:300} clusters in psgc)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}           reportNPA{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}1.selfenum {c |}{col 22}{res}{space 2} .0814279{col 34}{space 2} .1329742{col 45}{space 1}    0.61{col 54}{space 3}0.540{col 62}{space 4}-.1791967{col 75}{space 3} .3420526
{txt}{space 11}education {c |}{col 22}{res}{space 2} .2594845{col 34}{space 2} .0469676{col 45}{space 1}    5.52{col 54}{space 3}0.000{col 62}{space 4} .1674296{col 75}{space 3} .3515394
{txt}{space 20} {c |}
selfenum#c.education {c |}
{space 18}1  {c |}{col 22}{res}{space 2}-.0658385{col 34}{space 2} .0677562{col 45}{space 1}   -0.97{col 54}{space 3}0.331{col 62}{space 4}-.1986381{col 75}{space 3} .0669611
{txt}{space 20} {c |}
{space 15}_cons {c |}{col 22}{res}{space 2} .0921987{col 34}{space 2} .0970704{col 45}{space 1}    0.95{col 54}{space 3}0.342{col 62}{space 4}-.0980557{col 75}{space 3} .2824532
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}. eststo: logit claimHS selfenum##c.age, cl(psgc)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -2107.248}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1918.0094}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1917.9202}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1917.9202}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     3,044
{txt}{col 49}Wald chi2({res}3{txt}){col 67}= {res}    278.15
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-1917.9202{txt}{col 49}Pseudo R2{col 67}= {res}    0.0898

{txt}{ralign 80:(Std. Err. adjusted for {res:300} clusters in psgc)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}       claimHS{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}1.selfenum {c |}{col 16}{res}{space 2}-.1686269{col 28}{space 2} .2907049{col 39}{space 1}   -0.58{col 48}{space 3}0.562{col 56}{space 4}-.7383981{col 69}{space 3} .4011443
{txt}{space 11}age {c |}{col 16}{res}{space 2}-.0506256{col 28}{space 2} .0040092{col 39}{space 1}  -12.63{col 48}{space 3}0.000{col 56}{space 4}-.0584835{col 69}{space 3}-.0427678
{txt}{space 14} {c |}
selfenum#c.age {c |}
{space 12}1  {c |}{col 16}{res}{space 2} .0028317{col 28}{space 2} .0058241{col 39}{space 1}    0.49{col 48}{space 3}0.627{col 56}{space 4}-.0085833{col 69}{space 3} .0142466
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2} 2.560536{col 28}{space 2}  .204558{col 39}{space 1}   12.52{col 48}{space 3}0.000{col 56}{space 4}  2.15961{col 69}{space 3} 2.961462
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}. eststo: logit claimHS selfenum##c.education, cl(psgc)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2103.3308}  
Iteration 1:{space 3}log pseudolikelihood = {res:-469.73494}  
Iteration 2:{space 3}log pseudolikelihood = {res:-442.81235}  
Iteration 3:{space 3}log pseudolikelihood = {res:-442.70997}  
Iteration 4:{space 3}log pseudolikelihood = {res:-442.70992}  
Iteration 5:{space 3}log pseudolikelihood = {res:-442.70992}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     3,038
{txt}{col 49}Wald chi2({res}3{txt}){col 67}= {res}    259.40
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-442.70992{txt}{col 49}Pseudo R2{col 67}= {res}    0.7895

{txt}{ralign 86:(Std. Err. adjusted for {res:300} clusters in psgc)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}             claimHS{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}1.selfenum {c |}{col 22}{res}{space 2} .1260292{col 34}{space 2} .7424691{col 45}{space 1}    0.17{col 54}{space 3}0.865{col 62}{space 4}-1.329184{col 75}{space 3} 1.581242
{txt}{space 11}education {c |}{col 22}{res}{space 2} 4.227946{col 34}{space 2} .3754369{col 45}{space 1}   11.26{col 54}{space 3}0.000{col 62}{space 4} 3.492103{col 75}{space 3} 4.963789
{txt}{space 20} {c |}
selfenum#c.education {c |}
{space 18}1  {c |}{col 22}{res}{space 2}-.0927436{col 34}{space 2} .4818195{col 45}{space 1}   -0.19{col 54}{space 3}0.847{col 62}{space 4}-1.037093{col 75}{space 3} .8516053
{txt}{space 20} {c |}
{space 15}_cons {c |}{col 22}{res}{space 2}-5.904738{col 34}{space 2} .5655938{col 45}{space 1}  -10.44{col 54}{space 3}0.000{col 62}{space 4}-7.013282{col 75}{space 3}-4.796195
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}. *esttab using "selfvdirect_het.tex", se star(* .10 ** .05 *** .01) nodep mtitle("Sensitive" "Sensitive" "Placebo" "Placebo") label title(Heterogeneous Effects on Willingness to Report\label{c -(}tab:selfvdirhet{c )-}) note(Logistic regression with barangay-clustered SE.) replace
. 
. 
. *FIGURE 1 (appendix): Pilot Survey Summary Statistics
. use "NanesandHaim JEPS Pilot Rep.dta", replace
{txt}
{com}. 
. *It is understandable if any of these questions made you feel uncomfortable. Did any of them make you so uncomfortable that you think we should not ask them of people?
. tab q32uncomfortable

                  {txt}Q32.  (uncomfortable) {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
 Ang pagtanggap ng pera tuwing eleksyon {c |}{res}          3        5.00        5.00
{txt}Kung isusuplong mo ba sila sa mga kin.. {c |}{res}          7       11.67       16.67
{txt}Kung may kakilala kaming miyembro ng .. {c |}{res}         13       21.67       38.33
{txt}Wala naman sa mga katanungan ang labi.. {c |}{res}         37       61.67      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}         60      100.00
{txt}
{com}. *Do you believe that any of these questions are dangerous to ask? Remember that responses are completely anonymous and will only be shared with the researchers.
. tab q33dangerous

                      {txt}Q33.  (dangerous) {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
 Ang pagtanggap ng pera tuwing eleksyon {c |}{res}          2        3.33        3.33
{txt}Kung isusuplong mo ba sila sa mga kin.. {c |}{res}          7       11.67       15.00
{txt}Kung may kakilala kaming miyembro ng .. {c |}{res}         16       26.67       41.67
{txt}Wala naman sa mga katanungan ang deli.. {c |}{res}         35       58.33      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}         60      100.00
{txt}
{com}. *How did you feel about using the tablet to enter your answers? 
. replace q34selfconfused = "" if q34selfconfused == "x"
{txt}(25 real changes made)

{com}. tab q34selfconfused

 {txt}Q34.  (self.confused) {c |}      Freq.     Percent        Cum.
{hline 23}{c +}{hline 35}
Hindi naman nakalilito {c |}{res}         34       97.14       97.14
{txt}   Labis na nakalilito {c |}{res}          1        2.86      100.00
{txt}{hline 23}{c +}{hline 35}
                 Total {c |}{res}         35      100.00
{txt}
{com}. *How did you feel about answering the question based on the coin toss?
. replace q36rrconfused =  "" if q36rrconfused == "x"
{txt}(28 real changes made)

{com}. tab q36rrconfused

   {txt}Q36.  (rr.confused) {c |}      Freq.     Percent        Cum.
{hline 23}{c +}{hline 35}
Hindi naman nakalilito {c |}{res}         26       81.25       81.25
{txt}   Labis na nakalilito {c |}{res}          6       18.75      100.00
{txt}{hline 23}{c +}{hline 35}
                 Total {c |}{res}         32      100.00
{txt}
{com}. *How did you feel about using the tablet to enter your answers?
. replace q35selfhonest = "" if q35selfhonest == "x"
{txt}(20 real changes made)

{com}. tab q35selfhonest

{txt}Q35.  (self.honest) {c |}      Freq.     Percent        Cum.
{hline 20}{c +}{hline 35}
Mas lalong nabalisa {c |}{res}          2        5.26        5.26
{txt}      Mas napalagay {c |}{res}         29       76.32       81.58
{txt}    Walang kaibahan {c |}{res}          7       18.42      100.00
{txt}{hline 20}{c +}{hline 35}
              Total {c |}{res}         38      100.00
{txt}
{com}. *How did you feel about answering the question based on the coin toss?
. replace q37rrhonest = "" if q37rrhonest == "x"
{txt}(18 real changes made)

{com}. tab q37rrhonest

  {txt}Q37.  (rr.honest) {c |}      Freq.     Percent        Cum.
{hline 20}{c +}{hline 35}
Mas lalong nabalisa {c |}{res}          5       11.90       11.90
{txt}      Mas napalagay {c |}{res}         19       45.24       57.14
{txt}    Walang kaibahan {c |}{res}         18       42.86      100.00
{txt}{hline 20}{c +}{hline 35}
              Total {c |}{res}         42      100.00
{txt}
{com}. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Matthew\Dropbox\DHN Philippine Police\3. EGAP (POP - One Sorsogon Reloaded)\3.12 - Articles and Chapters\3.12.5 Self Enumeration and Sensitive Items\Replication Materials\SelfEnumerationdo_rep_log.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 1 May 2020, 09:35:47
{txt}{.-}
{smcl}
{txt}{sf}{ul off}